Contextual driver behavior monitoring

ABSTRACT

A database of high risk locations is formed and high risk causal factors for the high risk locations determined. Driver behavior is monitored at the sites in the database using data collection devices such as electronic logging devices or mobile phones to see if the drivers exhibit the same specific behaviors that are considered contributing factors to specific accident types at risk of occurrence at those sites. Warnings are provided to drivers approaching the specific sites to prompt behavioral changes which may further be monitored by the data collection devices.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.15/833,819, filed on Dec. 6, 2017, which is a continuation of U.S.application Ser. No. 15/250,681, filed on Aug. 29, 2016, now U.S. Pat.No. 9,849,887, which claims the benefit of U.S. Application No.62/210,986, filed Aug. 27, 2015.

BACKGROUND Technical Field

Driver behavior monitoring and prompting.

Description of the Related Art

Driver behavior is the leading cause of automobile crashes and modifyingdriver behavior is a focus of safety practitioners who implement safetyprograms to reduce crash rates. These efforts are of paramountimportance to large fleets that are faced with the potential to losemillions of dollars in legal liabilities due to crashes caused by thedrivers of their vehicles. One of the challenges to modifying driverbehavior lies in the initial detection of unsafe driver behavior priorto the actuality of a crash occurrence on a driving record. Methodsexist today to detect proxies for driver behavior through the monitoringof vehicle driving events such as speed (20130021148, 20130096731),hard-braking (20130096731, 20130274950), engine RPMs (20130245880),gear-shifting, lane departure warnings & roll stability activations.Other onboard event monitoring includes video capture of the driver todetect fatigue related events like nodding off, falling asleep anddrowsiness. Meta-data can also be collected including weather data(20120221216) and geo-location (20130073114) information in efforts tofilter out environmental factors affecting speed (bad weather, citycongestion) that may skew individual vehicle event data (actual drivingspeed lower than average; safe driving speed lower than posted limits)that lead to faulty analysis of individual driving behavior.

Some previous work on driver behavior depends on deriving proxies fordriver behavior based on onboard vehicle data inputs, and applying thoseproxies over a large time period and large population of drivers. Someprevious work relies on an indirect association between deviations frombroadly generalized averages and driver safety. Population statisticssuch as average speed, average gear changes, or average number ofhard-braking events create baseline proxies for safe driving, and largedeviations from averages are sometimes used to identify unsafe driversfor coaching. Hard braking events to avoid accidents with other vehiclescannot take into account the fault of other drivers. Drivers may drivefaster on the same routes based only on the delivery schedule andwhether it coincides with higher traffic volumes. All of these examplescan produce data that may erroneously skew negative perceptions of adriver's safe habits when in fact the data, when taken in a fullercontext, may point to the opposite. This is a potential shortcoming ofuncontextualized vehicle event data being used as proxies for driversafety. It relies on the law of averages to balance out irregular sampledata and highly variable conditions within population statistics withoutaddressing the fact that individual sample data continues to be reliedupon to identify high risk drivers.

Furthermore, a safety program that provides general training or coachingin response to a general comparison of individual driver behavior vs. apopulation of drivers fails to take into account the more powerfulcoaching opportunities that may be present in the moment, in the contextof a specific driving decision. For example, many in-cab navigationsystems will coach drivers to slow down if their speed exceeds theposted limit. This helps to remind drivers, if their attention hasdrifted, and also teaches drivers on regular routes about where speedlimits change. With the exception of video monitoring for drowsiness,the state of the art contains little consideration of instantaneousdriver coaching.

Some current methodologies often also collect vast amounts ofuncontextualized data covering the entire drive time of each driver.There are large data transmission costs associated with thesemethodologies and they are most extreme when vehicle events includevideo data.

This is not to say that these methodologies are without merit, it isonly to explain what may be their limitations.

BRIEF SUMMARY

There is provided in an embodiment a method of driver risk assessment,comprising maintaining a database of high risk locations, in whichdatabase each high risk location is associated with driver behaviorsthat are considered high risk in relation to the respective high risklocation, collecting driver behavior data for a vehicle that isindicative of driver behavior at a high risk location that is includedin the database, using a data collection device in the vehicle,comparing the driver behavior data to the driver behaviors consideredhigh risk in relation to the high risk location to determine a value orvalues representing driver risk, and flagging a driver whose driver riskvalue or values exceed predetermined risk criteria.

In a further embodiment the data collection device may detect that thevehicle is approaching a high risk location of the database of high risklocations, and on the data collection device detecting that the vehicleis approaching the high risk location, may display a visual warning tothe driver of the vehicle.

In a still further embodiment there is provided a method of improvingdriver safety, comprising maintaining a database of high risk locations,monitoring a location of a vehicle using a data collection device in thevehicle, and providing a visual warning display to a driver of thevehicle on the vehicle approaching a high risk location recorded in thedatabase of high risk locations.

In various embodiments there may also be any one or more of thefollowing: the visual warning display may comprise driving instructions;each high risk location recorded in the database of high risk locationsmay be associated with driver behaviors considered to be high risk inrelation to the respective high risk location, and driver behavior datamay be collected that is indicative of driver behavior at a high risklocation that is included in the database using the data collectiondevice, compared to the driver behaviors considered high risk inrelation to the high risk location to determine a value or valuesrepresenting driver risk, and a driver may be flagged whose driver riskvalue or values exceed predetermined risk criteria; each high risklocation recorded in the database of high risk locations may beassociated with driver behaviors considered to be high risk in relationto the respective high risk locations, and driver behavior data may becollected at the respective high risk locations, driver behavior datafrom vehicles whose drivers were shown the visual warning may becompared to driver behavior data from vehicles whose drivers were notshown the visual warning, the comparison being carried out with respectto the driver behaviors considered to be high risk in relation to therespective high risk locations, and on the comparison finding that thevisual warnings displays are not effective at reducing driver behaviorsconsidered high risk in relation to an accident location, the visualwarning displays may be ceased to be displayed at the high risklocation.

In various embodiments, the proposed method and system that implementsthe method, may use exception based analysis and contextualized vehicleevents. Random deviations of uncontextualized vehicle events have atenuous association with driver safety identification and must rely onthe collection of many data points per individual driver to creategeneralized risk profiles that could otherwise be identified sooner whendata is contextualized.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Embodiments will now be described with reference to the figures, inwhich like reference characters denote like elements, by way of example,and in which:

FIG. 1 is an overhead view of a highway interchange with a high-rolloverexit;

FIG. 2 shows of a rollover notification to be shown to all driversapproaching a high rollover zone at an exit ramp curve;

FIG. 3 shows a notification for a high rollover zone for a straight-awayzone profile;

FIG. 4 shows a high crash zone notification; and

FIG. 5 shows a server communicating with multiple clients over anetwork.

DETAILED DESCRIPTION

Immaterial modifications may be made to the embodiments described herewithout departing from what is covered by the claims.

In an embodiment, the proposed method and system creates a context layerof data more closely associated with high risk driving events bycollecting information on the riskiness of specific sites on thetransportation network and then measuring vehicle event data at onlythese high risk sites. Vehicle event data is contextualized withinspecific risk data from the roadway including historical crash rates,road curvatures, construction or work zone events, predictive analyticaldata on the likelihood of a crash on the specific road segment for thatmoment in time and compares it to data from other drivers and onboardsensor data to accurately identify high risk driving behavior with moreconfidence than any methodology based on onboard sensor data only. Theuse of contextual data as the basis for data collection is an efficientand effective approach to identifying high risk driving events becauseit only collects vehicle data at specific high-risk driving locations,and it collects data that is directly indicative of relative risk. Theresult is much more efficient method to identify high-risk driverbehavior.

Unlike averaged event data like average speed, number of gear shifts,hard braking events, etc., this approach is exception-based in anembodiment. An exception-based approach differs from a data collection,monitoring and processing tool based on universal data (always on,always collecting data) by using selective sampling (specific crashsites with known crash causalities). An embodiment of the system andmethod uses exception-based data collection (data only from riskylocations) as the sampling basis for population statistics and thereforecreates a closer link between vehicle event data and unsafe riverbehavior. The use of a subset of uncontextualized data targets driverbehavior when it most counts and driver analysis is based on neitheruncontextualized vehicle event data nor exception-based video data, butrather only data that is contextualized on the basis of the riskiness ofthe road at the moment the data is collected to more accurately identifyunsafe driver behavior.

The pre-selected sites are preferably based on published and availablecrash data which provides a data overlay to the GIS-based siteinformation (thus “contextualizing” it). Data is uncontextualized whencrash data is not mapped onto GIS information and is collectedcontinuously throughout the driving operation. Pre-scrutinized selectivesampling holds advantages over universal data collection/processingsystems because in principal that data is more closely associated withsafety risk and practically because data volumes/transmissiontime/communication costs/storage sizes are materially reduced. Thismethodology improves upon the limits of generalized uncontextualizedvehicle event data as a proxy for driver behavior because it onlyanalyzes event data contextualized by high-risk road elements. It alsoimproves upon exception-based video analytics methods because it doesnot rely solely on self-contained video-based machine-vision analyticsto identify an unsafe or fatigued driver, but allows for a broaderanalysis of risky driver behavior that may or may not be caused byfatigue.

In another aspect, in-cab driver notifications may be used on approachto a high risk road segment, in order to raise driver awareness of theupcoming road conditions and thereby encourage a safer transit throughthe high risk area.

In a further aspect, rigorous A/B testing of notification variations maybe carried out to find a more optimal notification strategy thatimproves driver outcomes. For example, compare cohorts of driversreceiving notifications 200 m, 400 m, or 600 m ahead of the risk area inorder to determine which spacing produces the slowest speed through therisk area, or the fewest hard breaking exceptions, or other measures ofrelative safety.

In an embodiment, the proposed system and method identifies specificregions and specific risk behaviors associated with those regions, so itcan effectively coach drivers to improve both their driving habits andtheir knowledge of risk areas on their routes. For example, a drivercould be presented simple notifications of upcoming risks, recommendedspeed, and so on. The notification may comprise driving instructions.For example, upon entering a risky curve, drivers that have not yetslowed sufficiently could be prompted again to slow down. Anotherexample of a possible driving instruction is a suggestion to take abreak based on HOS (hours of service, as a measure of time spentdriving) data exceeding a threshold.

Individual driver history may also be leveraged to modify the coachingprogram. For example, a driver approaching a high risk curve may becoached about the presence of the curve and the recommended maximumspeed through the curve. Over time, the driver may learn about thiscurve, and that learning may be reflected in the driver's speed patternthrough the curve. As the driver's behavior changes, the coachingprogram could switch to a less intrusive mode. Conversely, a driver thatcontinues to take a curve too quickly could be shown progressivelystronger coaching messages.

All of this history of driver behavior and coaching messages can be usedin aggregated reports to identify safe drivers and risky drivers, andprovide further coaching or training.

A database is formed listing specific high risk sites, the databaseincluding high risk (accident causal or potentially accident causal atthe specific sites) factors tied to the high risk patterns. A datacollection device in a vehicle, which may be for example an electroniclogging device or a mobile phone with an appropriate app installed,monitors driver behaviors at the specific sites to see if they exhibitthe same specific behaviors that are considered contributing factors tospecific accident types at high risk of occurrence at those sites. In anembodiment the data collection device may also contribute information tobuild the database.

In an embodiment, the data collection device may also passively monitorvehicle metrics such as speed, turn rate, acceleration and deceleration,and event data, in order to identify potential risk areas previouslyunknown in the risk area database. For example, passive monitoring mayreveal a turn that has a significant statistical variance in driverresponses, which may be indicative of a poorly marked or suddensharpening of the curve. Similarly, a statistical spike in brakingevents near a given intersection may indicate that the intersection haspoor visibility. These elements may be revealed as significant riskareas deserving of further monitoring and possibly active drivernotification even though no specific crash or rollover events arerecorded by law enforcement data. Where a visual warning display isprovided, the visual warning display, either as a whole or specificallyany driving instructions included with the display, may be modifiedbased on or provided based on a measurement of vehicle and driver statusat the time of provision of the warning display.

This section describes the process of identifying accident locations andcapturing fence segments that will be used for eventual alertnotification and data analysis.

Definitions

Segment: An entry or exit point on a section of road in one direction.

Path: Sequence of segments a vehicle travels.

Exit: Also referred to as entrance-ramp/on-ramp, exit-ramp/off-ramp thatallows vehicles to enter or exit a controlled access highway, freeway ormotorway.

Curve: Section of road when road changes direction from straight to e.g.S-curve.

Curve Center Point: the point located at the center of a curve halfwaybetween the beginning of a curve and the end of the curve.

Ramp: A ramp is a separate travel lane that branches of off or mergesinto a roadway.

A database indicating locations with high risk of accident, for examplecrashes or rollovers, will initially be built using accident informationobtained from state accident databases, accident experts and stateofficials. Other sources of accident information may also be used tobuild or improve the database, for example accident reports from usersor accident information collected automatically using electronic loggingdevices or a cellphone app. The data collection device may alsoidentify, for example, a high risk curve by tracking vehicle speed andturn rate through many example curves. Those curves with the highestlateral acceleration can be marked in the database as potential riskareas. An accumulation of data from many vehicles may indicate curvesthat are poorly marked, or unusually sharp, or where other roadconditions cause drivers to consistently produce higher lateralacceleration and therefore higher relative risk of a rollover event.This method of empirically identifying risk areas may in fact produce arich data set that is unknown to law enforcement, and for which no(relatively rare) rollover events have actually occurred. This would beespecially useful in identifying risky curves in new roadways and lesstravelled roadways. The database may model the geographic entitiesrepresenting the high risk locations. The ATRI™ high rollover database,which has database points in 32 states for 277 hot spot sites in total,may be used for example as a starting point. It is contemplated that thesites from the ATM database will be validated by state agencies andaccident reporting experts before a site is identified and created as ahigh rollover site in the database of high risk locations, in order toensure the accuracy and specificity of the sites. The site shouldidentify the specific segment location, type—exit, curve, zones, andposted speed limit (if available) at the area for the trucks. In the ATMdatabase each site is marked by a point on a map and identified by aname with the number of rollovers. Example ATRI Data point: State:Texas, Site Name: US 59/South St and US 59/SR224, Number of Rollovers:9.

The risk sites may be entered into the system using existing Geofencetechnology and tools. Geofences may describe geographic regions using avariety of geometric models (enclosing polygon, proximity to a point,proximity to a roadway, direction of travel, etc.) The number ofgeo-fences for a site and location is left to implementation. Forrollovers the site data may preserve for example:

-   -   a. Location and specific rollover segment beginning and end    -   b. Date created in the system    -   c. Type of site, e.g.:        -   i. Curved: Road is curved        -   ii. Exit ramp: Exit on a highway        -   iii. Entry ramp: Entry ramp on a highway        -   iv. Straight: straight road identified as a high rollover            site due to, e.g. weather conditions.    -   d. Posted speed or appropriate speed limit for trucks in this        zone    -   e. Known historical count and duration of incidents    -   f. Data variations that are specific to a vehicle type. For        example, it may indicate different safe speed limits for trucks        of various lengths, numbers of axles, loaded and unloaded        trailers, and so on.

The geo-fences should be created to satisfy following requirements:

a. For a geofence intended to trigger a notification for a driver, thegeofence should be positioned so that notice is provided close enough tothe site so driver can react smoothly, adjusting driving behaviorwithout being so close as to create a safety risk via overreaction whenalready in a turn. Notifications should be close enough to the risk areaso that driving behavior modification is maintained throughout the riskarea.

b. For a geofence intended to trigger measuring and recording ofrollover related data, the geofence should provide triggers to startmeasuring and recording rollover related data at a suitable location tobegin measuring and recording the data.

There is no requirement to capture data in any other path that is notidentified as high rollover path. In an embodiment, a single geofencemay be used to trigger both a notification for a driver and to triggermeasuring and recording rollover related data. Additional geofences maybe used for example to stop data collection or to trigger a notificationthat the driver is exiting a high rollover zone. Vehicle location may bemonitored using for example GPS. Vehicle location data may be used todetermine if a vehicle has entered a geofence. Vehicle speed and bearinginformation may also be collected, also for example using GPS. Locationat which key events such as deceleration occur may also be measured.Other data such as engine bus events may also be collected, for examplehard braking events or steering corrections. Other sensors may also beapplied such as accelerometers or other devices that contributeenvironmental data. For system efficiency, data in paths other thanthose identified as high risk paths may be discarded at the in-cabdevice. In another embodiment, data may be captured continuously at thein-cab device, and a combination of heuristic algorithms and compressioncould be applied to extract unmapped areas of interest and transmit themto a server for further analysis.

In an embodiment, driver behavior and other sensor data may be examinedin real time even outside of known risk areas. This may be implementedas a passive monitoring system whose purpose is to determine risk areasthat have not previously been documented. Once identified, those newrisk areas could be validated and then added to the risk database. In anembodiment, the data could be monitored continuously by the in-cabdevice, but only sent to the server in the event of risk-indicativeconditions such as hard braking, steering corrections at speed or highlateral acceleration. In a further embodiment, transmission of the datato the server may be based on a threshold of a risk-indicative conditionset below a level of the risk-indicative condition at which thecondition would be of particular concern. In a still further embodiment,the data may be transmitted to the server regardless of whether arisk-indicative condition is detected.

FIG. 1 shows an example of high-level spatial timeline as a vehicletakes a high rollover exit ramp. As shown in FIG. 1, an example highrollover zone is an exit ramp 12 of an interchange 10 between a firsthighway 14 and a second highway 16. The exit ramp 12 in question is anexit ramp for vehicles turning right from first highway 14 to secondhighway 16. A data collection initial fence 18 starts data collection. Arollover notification fence 20 triggers a rollover notification to thedriver. A further fence 22 might include a navigation direction, i.e. ageofence that is sensitive to the direction of travel of the vehicle.This is used to prevent traffic flowing in the wrong direction frominadvertently activating a fence (in case GPS errors make it appear thata vehicle has drifted into the fence). The driver may slow speed inresponse to the rollover notification. This slowing at for examplelocation 24 is detected by the data collection. The data collection anddriver notification are triggered in this example before it is possibleto determine whether or not the truck will take the exit ramp 12. Afurther geofence 26 located after the ramp detects if the vehicle hastaken the ramp and stops data collection. There may also be one or moregeofences (not shown) on the ramp itself to determine if the vehicle hastaken the ramp. Data collection may also be stopped by, for example, atimeout. A still further geofence 28 may also detect if the vehicle hasproceeded straight along highway 14; and yet more geofences may detectif the vehicle has taken another exit ramp such as ramp 30. Note that ifa driver does not take an exit benchmarking data such as speed of thetruck may still be captured. This will help benchmark general populationbehavior near the high rollover area. However, data from drivers takingan exit should be distinguished from data from drivers not taking theexit. There may be additional rollover zones in the same intersection ornearby intersections and corresponding geofences may be provided for allsuch rollover zones.

Where the application is configured to display a warning message to adriver, it may display a message specific to the type of accident forwhich the site is high-risk, for example High Rollover or High Crashzones. If the warning message is to be displayed for an exit before itis known that the driver will take the exit, an indication may show thatthe warning message is for the exit. The warning message may be providedfor example in the form of a visual display representing a road sign. Arecognizable sound may be provided with the visual display. A reminderto slow down may be provided to the driver with the visual display.Messages may be enhanced by displaying the vehicle's current speedand/or a target speed. Follow-up messages may be shown if, for example,a driver takes a high risk curve but fails to slow on entering thecurve. A further notification may be provided to the driver on leavingthe high risk area.

In an embodiment, a client-server system is used with a client programin the vehicle instantiated in e.g. an application on a mobile phone oran electronic logging device platform. A server is typically in contactwith all clients via a telecommunications network. The databaseconcerning high risk locations may be maintained on the server but alsosynchronized to all clients. Data concerning geofences, UI descriptorsand logic elements may be synchronized to a cache in the client so thatthe warning system may operate autonomously in case of a network issue.An extensible event-based architecture may be used that recordsprogressively higher level semantic events (gps location, fence event,high rollover event, ui event, etc.); events span client and servercomponents seamlessly; logic elements listen for events, process logic,and send new events. Events are delivered to the server for analysis. Inan embodiment, location/Fence events drive logic on the client device.UI events may show notifications. GPS events deliver speed data toanalytics logic on client. A “Risk Visit” event may deliver a summary ofa visit to a high risk location and the corresponding driver reaction tothe server. FIG. 5 shows a server 50 communicating with multiple clients54 over network 52.

The system may monitor driver reaction and behavior while in the highrisk area (e.g. monitor vehicle speed before and after the notificationand while in the high risk region). The system may collect dataconcerning a visit to a high risk location, such as the vehicle, driver,location, whether and when/where a notification was given, and vehiclespeed data) and transfer the data to the server for analysis. The serverwould collect data about each vehicle visit to a risk area. This mightinclude, for example, the time and location of entry to and exit fromthe area, vehicle speed and bearing on entry and exit, a series oflocation, speed, and bearing data tuples through curves, braking events,and so on. All of these data elements could be collected into anaggregate “visit” record, along with specifics about the vehicle such asnumber of axles, length, weight, and so on. Furthermore the individualrisk events or visits could be aggregated in several dimensions: by riskarea, by vehicle, by driver, by vehicle type, and so on. The aggregateddata could indicate statistical data such as average speed etc. Usefulreporting could then be generated including, for example, fleetperformance with respect to global averages, individual driverperformance, listings of drivers needing further training, mostproblematic sites or curves, and so on.

The system may capture data that will help calibrate and benchmarkaccident causal driver behavior at high risk sites in the database. Asthe actual accident rate is generally extremely low, actual accidentsare not statistically adequate to determine risk of individual drivers.Accident causal driver behavior may be used as a proxy for the discreterisk occurrence. For a high-rollover site where speed and turn radiusare determined to be primary accident causal driver behavior metrics.Other factors such as load balance, center of gravity, etc. may also bedetermined to be influential in rollover events, and also trafficconditions and other environmental conditions. Not all of these factorsmay be easily measurable, so the data that is collected can be viewed asa statistical exercise against the site's determined primarycrash-causal factors—e.g. looking at fleet-wide data, and comparing asignificant history of events for one driver to the fleet averages.Collecting a large amount of data will help as lots of historical datawill provide a smooth background against which “outlier” drivers willstand out. The system may provide a combination of real-time responseand historical data. One goal of the data collection is to validate thatthe warning message service is affecting behavior but not causingadditional problems for drivers. Analyzing the speed at the time thealert was displayed and just after should provide detail of whetherthere was rapid deceleration caused by hard braking. This analysis needsto be done only once; for a period of time during the introduction ofthe service and may be used for internal use only. Examples of such datathat may be collected include:

-   -   a. Speed related data points:        -   i. Average speed from beginning of curve/zone to end of            curve/zone        -   ii. Speed at center of curve/zone        -   iii. GPS speed at configurable time before alert            notification was shown to the driver. (Start at 5 seconds)        -   iv. GPS speed at a configurable time after the alert            notification was shown to the driver. Start with a            configurable time of 5 seconds.        -   v. GPS speed at high rollover and high crash zone beginning            of curve/zone and end of curve/zone fences.        -   vi. Maximum speed    -   b. Hard breaking i.e. sudden deceleration data either calculated        by engine bus data if available    -   c. Sudden Acceleration data either calculated by engine bus data        if available    -   d. Maximum deceleration    -   e. Maximum lateral acceleration (may be computed from speed and        turn radius)    -   f. Current HOS (hours of service) available duty time    -   g. Actuation of vehicle roll stability system via engine bus,        FMS device or FMS back office

Multiple different types of notifications, training exercises etc. maybe compared with one another by e.g. creating two cohorts of 500 driverseach and show a given notification to only one group. Observe anydifferences in driver behavior between the two cohorts. A notificationtype or training exercise for which better results are observed may beretained and one for which worse results are observed may be discarded.

Data collection may be carried out by an application on a datacollection device, for example on an electronic logging device or mobilephone.

The application may have an option to enable/disable data capture typesfor sites.

The application may keep enough data to provide reporting on alertnotifications (product safety—notifications stats) and driver behavior(driver risk).

The application may have an option to enable/disable notifications foreach site.

The application may have an option to enable/disable notifications atspecific sites for specific fleet

The application may modify notification frequency and nature accordingto individual driver history and success in navigating the then-presentrisk area.

The application may modify notifications and other attributes accordingto vehicle type, vehicle length, number of axles, present weight, andother attributes.

The application may define following service types at the carrier(fleet) level:

a. Rollover Analytics turn ON or OFF at fleet level: This will turn onrollover data collection at the device.

b. Rollover Notifications turn ON or OFF at fleet level.

c. Rollover Analytics Reporting turn ON or OFF at fleet level. Thisswitch will turn on reporting for reporting fleet. The reports will bemade available to customer via Fleet Management Portal. The FleetManagement Portal (FMP) is, for example, a web portal that allows fleetmanagement or fleet safety personnel to log in and recall data specificto their fleet vehicles. This could also include on-demand generation ofvarious reports. This could also include options to enable/disablefeatures at the fleet, vehicle, or driver level.

The application may collect data to benchmark and learn driver behaviorat the sites in the database, first without warning notifications.Reports may be generated in a web portal, either a purpose-builtapplication or a general purpose reporting tool and data warehouse, orsome other combination of tools. Information may be collected to assessthe overall behavior tied to primary crash causal factors of the drivingpopulation through high rollover sites:

a. Mean, standard deviation of speed

b. Distribution of speed profiles

Information may also be collected to assess the speed profile of driversthrough rollover sites:

a. Mean speed through curve/zone (Listed for example in descending orderof standard distributions from mean)

b. Speed at center of curve/zone (Listed for example in descending orderof standard distributions from mean)

c. Lateral acceleration, for example maximum lateral acceleration in acurve or zone. This may be computed from instantaneous speed and turnrate. Maximum lateral acceleration in a curve is correlated withrollover probability. (Listed for example in descending order ofstandard distributions from mean)

d. A report may be generated to highlight any drivers with metrics equalor greater than 2 standard deviations ABOVE mean speed measures

Data may also be collected to determine the changes in driver behaviorin response to notifications of high-risk zones. For example theapplication may calculate average percentage reduction in center pointspeed and mean curve/zone speed of all drivers receiving notificationsversus drivers receiving no notifications, for example the same driversat the same locations (where data exists) prior to the implementation ofthe notification service (pre-notification baseline). The calculationsmay be carried out separately for high crash, high rollover (with curve)and high rollover (no curve) zones. This may be determined at an overalllevel as a system validation measure as well as to provide fleet andindividual measures for the subset of the population for whichnotification is provided. In order to determine if notifications arecausing unsafe behavior such as hard breaking after notification ispresented, the application may also calculate average deceleration ofvehicles based on speed data prior to and just after notification. Rapiddeceleration may also be detected by monitoring engine bus data or otheravailable sensors.

The application may analyze fleet-wide data to generate reports that maybe sent to carriers via any suitable method. The reports may providefleet-wide information concerning driver response to notifications,benchmark a fleet characteristic at an accident zone type or location,compare the fleet against industry norm, and provide a coaching reportthat identifies drivers who need training to improve safe driving habitsat such zones. One example criterion to identify such drivers may bethat the driver is consistently in top nth percentile for speed throughrisk areas within a cohort of similar drivers. For example, thosedriving a tandem dump truck will have different speed targets from thosedriving a delivery van or a 50′ trailer. The fleet characteristic mayinclude for example, fleet mean at center point speed and averagecurve/zone speed in rollover zones for all drivers receivingnotifications and % change from pre-notification baseline, and fleetmean at center point speed and average curve/zone speed in rolloverzones for all drivers not receiving notification. The report may alsoprovide riskiest location alerts where a specific fleet's driversexhibit risky driving behavior as a group more than industry-widedrivers.

The application may display the notifications specific to a site type:High Rollover or High Crash Zone. The signage may be designed to adhereto MUTCD

[Manual on Uniform Traffic Control Devices] Guidelines.

The application may also provide the ability to dismiss a notificationwithin the current framework.

For example, FIG. 2 shows a mock up of a rollover notification 42 shownon a display 40 to all drivers approaching a high rollover zone at anexit ramp curve. The EXIT notice 44 notifies drivers it is onlyapplicable to vehicles that exit the mainline and enter the zone. It isgiven to all drivers in advance of whether it is known the driver willactually take the exit as it is impossible to know which drivers willtake the exit ramp 3-5 seconds in advance of the exit ramp. FIG. 3 showsa mock up of a notification 46 for a high rollover zone for astraight-away zone profile. Note that there is no EXIT designation. FIG.4 shows a mock up of a High Crash Zone notification 48.

A notification may include an indication of how long a rollover zone is,so that the driver knows when to get back to normal speeds, or an end ofzone notification may be provided. Alternatively, no indication of wherethe zone ends may be provided, and the driver left to determine when toreturn to normal speeds on their own.

An audible sound may be provided with accident zone notifications tohelp alert drivers. The audible sound may be, for example, an audiblesound used for heads up notifications.

The application may provide notifications at a configurable distancefrom ramp entrances. For exit lane curves the beginning of the zone maybe the point of the exit ramp gore. For crash zones and high rolloverzones (straightaways) the beginning of the zone may be the state definededge or boundary of the high-crash data. Notifications may be placed 5seconds (at posted speed limits) prior to entering the high-riskcurve/zone as suggested by MUTD or a configurable alternative distanceor time before the entrance to the zone. The optimal distance or timemay be determined empirically through experimentation and monitoring ofdriver responses. As the application may have access to actual speeddata from a vehicle, the application may in an embodiment provide anotification a configurable time before the entrance to the zoneaccording to the actual speed of the vehicle rather than speed at postedspeed limits.

Notifications may be configured to be displayed for a minimum andmaximum period. For example, a notification may require a minimumdisplay time of 3 seconds, but also remain visible for longer (forexample up to 10 seconds) if no other priority use of the display isrequested.

Notifications may be preferably shown for a short time, e.g. 2-3seconds, so they are not distracting and do not interfere withnavigation system directions as most of these signs are near entry orexit ramps. The application may provide the capability to configuredurations of notification display and distance of notification displayfrom high-risk zones at fleet, partner and site level.

A fleet management portal may provide an interface to allow individualcarriers to turn on and off the High Rollover Analytics Service, HighRollover Notification Service and High Rollover Reporting Service, andindividual vehicles to turn on and off the High Rollover AnalyticsService and High Rollover Notification Service. A dashboard of e.g.rollover related information for the specific carrier may also beprovided, and rollover related carrier reports for the carrier.

The application may be provided on any of multiple platforms, includingfor example fleet management devices, navigation devices, in-vehiclesystems and mobile devices.

In the claims, the word “comprising” is used in its inclusive sense anddoes not exclude other elements being present. The indefinite article“a” before a claim feature does not exclude more than one of the featurebeing present. Each one of the individual features described here may beused in one or more embodiments and is not, by virtue only of beingdescribed here, to be construed as essential to all embodiments asdefined by the claims.

1. A method of driver risk assessment, comprising: maintaining adatabase of high risk locations, in which database each high risklocation is associated with driver behaviors that are considered highrisk in relation to the respective high risk location; collecting driverbehavior data for a vehicle that is indicative of driver behavior at ahigh risk location that is included in the database, using a datacollection device in the vehicle; comparing the driver behavior data tothe driver behaviors considered high risk in relation to the high risklocation to determine a value or values representing driver risk; andflagging a driver whose driver risk value or values exceed predeterminedrisk criteria.
 2. The method of claim 1 further comprising the datacollection device detecting that the vehicle is approaching a high risklocation of the database of high risk locations, and on the datacollection device detecting that the vehicle is approaching the highrisk location, displaying a visual warning to the driver of the vehicle.3. A method of improving driver safety, comprising: maintaining adatabase of high risk locations; monitoring a location of a vehicleusing a data collection device in the vehicle; and providing a visualwarning display to a driver of the vehicle on the vehicle approaching ahigh risk location recorded in the database of high risk locations. 4.The method of claim 3 in which each high risk location recorded in thedatabase of high risk locations is associated with driver behaviorsconsidered to be high risk in relation to the respective high risklocation, the method further comprising the steps of: collecting driverbehavior data that is indicative of driver behavior at a high risklocation that is included in the database using the data collectiondevice; comparing the driver behavior data to the driver behaviorsconsidered high risk in relation to the high risk location to determinea value or values representing driver risk; and flagging a driver whosedriver risk value or values exceed predetermined risk criteria.
 5. Themethod of claim 4 in which the visual warning comprises drivinginstructions.
 6. The method of claim 5 further comprising contextualmodification of the driving instructions based on instantaneousmeasurement of vehicle and driver status.
 7. The method of claim 6comprising displaying the driving instructions based on vehicle speedexceeding a specified limit, or based on driver hours of service (HOS)data exceeding a given threshold.
 8. The method of claim 3 in which thevisual warning display comprises driving instructions.
 9. The method ofclaim 8 further comprising contextual modification of the drivinginstructions based on instantaneous measurement of vehicle and driverstatus.
 10. The method of claim 9 further comprising displaying thedriving instructions based on vehicle speed exceeding a specified limit,or based on driver hours of service (HOS) data exceeding a giventhreshold.
 11. A method of improving driver safety, comprising:maintaining a database of high risk locations; monitoring a location ofa vehicle using a data collection device in the vehicle; providing avisual warning display to a driver of the vehicle on the vehicleapproaching a high risk location recorded in the database of high risklocations, the visual warning display comprising driving instructions;and displaying the driving instructions based on driver hours of service(HOS) data exceeding a given threshold.
 12. The method of claim 11further comprising contextual modification of the driving instructionsbased on instantaneous measurement of vehicle and driver status.
 13. Themethod of claim 11 in which each high risk location recorded in thedatabase of high risk locations is associated with driver behaviorsconsidered to be high risk in relation to the respective high risklocation, the method further comprising: collecting driver behavior datathat is indicative of driver behavior at a high risk location that isincluded in the database using the data collection device; comparing thedriver behavior data to the driver behaviors considered high risk inrelation to the high risk location to determine a value or valuesrepresenting driver risk; and flagging a driver whose driver risk valueor values exceed predetermined risk criteria.